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Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study.

Hung-Cuong Nguyen1, Thi-Hao Nguyen1, Rafał Scherer2

  • 1Faculty of Engineering Technology, Hung Vuong University, Viet Tri City 35100, Vietnam.

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Summary

This survey reviews deep learning for human activity recognition (HAR) using 3D skeleton data. It covers Recurrent Neural Networks, Convolutional Neural Networks, Graph Convolutional Networks, and Hybrid Deep Neural Networks from 2019-2023.

Keywords:
3D human pose/skeletonKLHA3D 102 datasetKLYoga3D datasetconvolutional neural networks (CNN)deep neural networksgraph convolution networks (GCN)human activity recognitionrecurrent neural networks (RNN)

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human Activity Recognition (HAR) is crucial for human-machine interaction and monitoring.
  • Skeleton-based HAR offers intuitive and effective applications.
  • A comprehensive understanding of current deep learning approaches is vital for product development.

Purpose of the Study:

  • To conduct a full survey of deep learning methods for 3D skeleton-based HAR.
  • To analyze and compare different deep learning architectures including RNN, CNN, GCN, and Hybrid-DNN.
  • To provide an up-to-date overview of models, datasets, metrics, and results from 2019 to March 2023.

Main Methods:

  • Systematic literature review of deep learning models for 3D skeleton-based HAR.
  • Categorization of methods based on network types: RNN, CNN, GCN, and Hybrid-DNN.
  • Comparative analysis of selected deep learning networks on KLHA3D 102 and KLYOGA3D datasets.

Main Results:

  • Detailed presentation of HAR models, datasets, and performance metrics from 2019-2023.
  • Comparative study highlighting the performance of CNN, GCN, and Hybrid-DNN approaches.
  • Analysis of strengths and weaknesses of different deep learning architectures for 3D skeleton-based HAR.

Conclusions:

  • Deep learning, particularly GCNs and Hybrid-DNNs, shows significant promise for 3D skeleton-based HAR.
  • The survey provides valuable insights for researchers and developers selecting HAR solutions.
  • Further research can focus on refining hybrid models and exploring novel feature extraction techniques.